ZooBuilder: 2D and 3D Pose Estimation for Quadrupeds Using Synthetic Data
Not on the map. Why?
Abstract (arXiv)
This work introduces a novel strategy for generating synthetic training data for 2D and 3D pose estimation of animals using keyframe animations. With the objective to automate the process of creating animations for wildlife, we train several 2D and 3D pose estimation models with synthetic data, and put in place an end-to-end pipeline called ZooBuilder. The pipeline takes as input a video of an animal in the wild, and generates the corresponding 2D and 3D coordinates for each joint of the animal's skeleton. With this approach, we produce motion capture data that can be used to create animations for wildlife.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 1 Sep 2020.
Builds on
No reference list yet.
Built on by
Citing papers per year: 2021: 1; 2022: 1; 2023: 0; 2024: 1.
3 papers.
- 2024Generative Zoo11 Dec 2024
- 2022AI and 6G into the Metaverse: Fundamentals, Challenges and Future Research Trends23 Aug 2022
- 2021T-LEAP: Occlusion-robust pose estimation of walking cows using temporal information16 Apr 2021